Monitoring and Forecasting Air Pollution Levels by Exploiting Satellite, Ground-Based, and Synoptic Data, Elaborated with Regression Models
This paper presents some of the results of a project that aimed at the design and implementation of a system for the spatial mapping and forecasting the temporal evolution of air pollution from dust transport from the Sahara Desert into the eastern Mediterranean and secondarily from anthropogenic so...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2017/2954010 |
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| author | Silas Michaelides Dimitris Paronis Adrianos Retalis Filippos Tymvios |
| author_facet | Silas Michaelides Dimitris Paronis Adrianos Retalis Filippos Tymvios |
| author_sort | Silas Michaelides |
| collection | DOAJ |
| description | This paper presents some of the results of a project that aimed at the design and implementation of a system for the spatial mapping and forecasting the temporal evolution of air pollution from dust transport from the Sahara Desert into the eastern Mediterranean and secondarily from anthropogenic sources, focusing over Cyprus. Monitoring air pollution (aerosols) in near real-time is accomplished by using spaceborne and in situ platforms. The results of the development of a system for forecasting pollution levels in terms of particulate matter concentrations are presented. The aim of the present study is to utilize the recorded PM10 (particulate matter with aerodynamic diameter less than 10 μm) ground measurements, Aerosol Optical Depth retrievals from satellite, and the prevailing synoptic conditions established by Artificial Neural Networks, in order to develop regression models that will be able to predict the spatial and temporal variability of PM10 in Cyprus. The core of the forecasting system comprises an appropriately designed neural classification system which clusters synoptic maps, Aerosol Optical Depth data from the Aqua satellite, and ground measurements of particulate matter. By exploiting the above resources, statistical models for forecasting pollution levels were developed. |
| format | Article |
| id | doaj-art-8ac7e563c8fd4fff8de352add4f8ca28 |
| institution | OA Journals |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-8ac7e563c8fd4fff8de352add4f8ca282025-08-20T02:09:24ZengWileyAdvances in Meteorology1687-93091687-93172017-01-01201710.1155/2017/29540102954010Monitoring and Forecasting Air Pollution Levels by Exploiting Satellite, Ground-Based, and Synoptic Data, Elaborated with Regression ModelsSilas Michaelides0Dimitris Paronis1Adrianos Retalis2Filippos Tymvios3The Cyprus Institute, 2121 Aglantzia, Nicosia, CyprusInstitute for Astronomy, Astrophysics, Space Application & Remote Sensing, National Observatory of Athens, 15236 Athens, GreeceInstitute for Environmental Research and Sustainable Development, National Observatory of Athens, 15236 Athens, GreeceThe Cyprus Institute, 2121 Aglantzia, Nicosia, CyprusThis paper presents some of the results of a project that aimed at the design and implementation of a system for the spatial mapping and forecasting the temporal evolution of air pollution from dust transport from the Sahara Desert into the eastern Mediterranean and secondarily from anthropogenic sources, focusing over Cyprus. Monitoring air pollution (aerosols) in near real-time is accomplished by using spaceborne and in situ platforms. The results of the development of a system for forecasting pollution levels in terms of particulate matter concentrations are presented. The aim of the present study is to utilize the recorded PM10 (particulate matter with aerodynamic diameter less than 10 μm) ground measurements, Aerosol Optical Depth retrievals from satellite, and the prevailing synoptic conditions established by Artificial Neural Networks, in order to develop regression models that will be able to predict the spatial and temporal variability of PM10 in Cyprus. The core of the forecasting system comprises an appropriately designed neural classification system which clusters synoptic maps, Aerosol Optical Depth data from the Aqua satellite, and ground measurements of particulate matter. By exploiting the above resources, statistical models for forecasting pollution levels were developed.http://dx.doi.org/10.1155/2017/2954010 |
| spellingShingle | Silas Michaelides Dimitris Paronis Adrianos Retalis Filippos Tymvios Monitoring and Forecasting Air Pollution Levels by Exploiting Satellite, Ground-Based, and Synoptic Data, Elaborated with Regression Models Advances in Meteorology |
| title | Monitoring and Forecasting Air Pollution Levels by Exploiting Satellite, Ground-Based, and Synoptic Data, Elaborated with Regression Models |
| title_full | Monitoring and Forecasting Air Pollution Levels by Exploiting Satellite, Ground-Based, and Synoptic Data, Elaborated with Regression Models |
| title_fullStr | Monitoring and Forecasting Air Pollution Levels by Exploiting Satellite, Ground-Based, and Synoptic Data, Elaborated with Regression Models |
| title_full_unstemmed | Monitoring and Forecasting Air Pollution Levels by Exploiting Satellite, Ground-Based, and Synoptic Data, Elaborated with Regression Models |
| title_short | Monitoring and Forecasting Air Pollution Levels by Exploiting Satellite, Ground-Based, and Synoptic Data, Elaborated with Regression Models |
| title_sort | monitoring and forecasting air pollution levels by exploiting satellite ground based and synoptic data elaborated with regression models |
| url | http://dx.doi.org/10.1155/2017/2954010 |
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